Evolutionary computation in combinatorial optimization

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This collection explores various advanced optimization techniques and algorithms across multiple domains. It includes a hybrid genetic algorithm within a branch-and-cut framework aimed at addressing the minimum graph bisection problem, and examines the trade-off between diversity and quality in multi-objective workforce scheduling. The evolution of particle swarm optimization algorithms is discussed, alongside a tabu search algorithm designed for optimizing gas distribution networks. Additionally, the design of a retail chain stocking policy using a hybrid evolutionary algorithm is presented, as well as parametrized GRASP heuristics for three-index assignment problems. A memetic algorithm utilizing bucket elimination is applied to the still life problem, while the effects of scale-free and small-world topologies on binary-coded self-adaptive competitive evolutionary algorithms are analyzed. The collection also tackles the traveling salesman problem with particle swarm optimization, and introduces a hierarchical cellular genetic algorithm. It focuses on improving graph coloring algorithms through novel representations and minimizing makespan on single batch processing machines using a hybrid genetic approach. New computational results for the nurse scheduling problem via a scatter search algorithm are included, along with a fast EAX algorithm that considers population diversity for traveling salesman problems. Other topics